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METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine | IEEE Journals & Magazine | IEEE Xplore

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine


Abstract:

Goal: The aim of this paper is to propose the design and implementation of next-generation enterprise analytics platform developed at the Houston Methodist Hospital (HMH)...Show More

Abstract:

Goal: The aim of this paper is to propose the design and implementation of next-generation enterprise analytics platform developed at the Houston Methodist Hospital (HMH) system to meet the market and regulatory needs of the healthcare industry. Methods: For this goal, we developed an integrated clinical informatics environment, i.e., Methodist environment for translational enhancement and outcomes research (METEOR). The framework of METEOR consists of two components: the enterprise data warehouse (EDW) and a software intelligence and analytics (SIA) layer for enabling a wide range of clinical decision support systems that can be used directly by outcomes researchers and clinical investigators to facilitate data access for the purposes of hypothesis testing, cohort identification, data mining, risk prediction, and clinical research training. Results: Data and usability analysis were performed on METEOR components as a preliminary evaluation, which successfully demonstrated that METEOR addresses significant niches in the clinical informatics area, and provides a powerful means for data integration and efficient access in supporting clinical and translational research. Conclusion: METEOR EDW and informatics applications improved outcomes, enabled coordinated care, and support health analytics and clinical research at HMH. Significance: The twin pressures of cost containment in the healthcare market and new federal regulations and policies have led to the prioritization of the meaningful use of electronic health records in the United States. EDW and SIA layers on top of EDW are becoming an essential strategic tool to healthcare institutions and integrated delivery networks in order to support evidence-based medicine at the enterprise level.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 62, Issue: 12, December 2015)
Page(s): 2776 - 2786
Date of Publication: 26 June 2015

ISSN Information:

PubMed ID: 26126271

Funding Agency:


I. Introduction

The clinicians and researchers at the Houston Methodist Hospital (HMH) system use multiple data sources to acquire the data for research and quality improvement purposes as no single infrastructure or database exists that could provide them with ease the data required for their research. The HMH system is a home to seven hospitals and operates approximately nine major categories of clinical databases. Current methods of obtaining data from all these locations and vendors for preparatory-to-research questions often involves laborious time-consuming manual extracts and cleansing of data for specific projects. It is recognized that the current process is cumbersome, costly, and time consuming and adds no intrinsic value to the research being undertaken. This leads investigators to spend a lot of unproductive time in negotiating and waiting for data instead of conducting the research. Worse, the data ultimately delivered often are incomplete, depending on the understanding and knowledge of the person retrieving the data. In many institutions, a “gray market” for data could develop, as researchers find unofficial workarounds to obtain data they need for their work. This “gray market” approach could lead to compliance and security risks, as isolated silos of patient data evolve in different parts of the healthcare organization without formal oversight for Health Insurance Portability and Accountability Act (HIPAA) and Institutional Review Board (IRB) compliance, and outside of the processes for protecting data from misuse or breach. HMH researchers need access to vast pools of patient data to develop and test their scientific hypotheses, so the making of a solitary integrated data system would provide a huge opportunity for an expanded number of biomedical research projects, including large-scale projects.

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